CN111965546A - Lithium battery capacity and initial SOC estimation method based on OCV curve reconstruction - Google Patents

Lithium battery capacity and initial SOC estimation method based on OCV curve reconstruction Download PDF

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CN111965546A
CN111965546A CN202010917188.1A CN202010917188A CN111965546A CN 111965546 A CN111965546 A CN 111965546A CN 202010917188 A CN202010917188 A CN 202010917188A CN 111965546 A CN111965546 A CN 111965546A
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battery
ocv curve
capacity
curve
ocv
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CN111965546B (en
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唐勇
孙金磊
姜涛
唐传雨
王天如
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Nanjing University of Science and Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Abstract

The invention discloses a battery capacity and initial SOC estimation method based on OCV curve reconstruction, wherein a battery pack comprises N to-be-estimated monomers of the same type and the same batch, and the method comprises the following steps: carrying out a low-current capacity test on the battery pack to obtain a battery IC curve, and collecting characteristic peak and valley points of the curve; combining the coordinates of the peak point and the valley point to obtain characteristic points of the OCV curve of the battery, and judging incomplete areas of the OCV curve by using the characteristic points; changing the characteristic points of the corresponding OCV curves among the monomers; and (4) with the minimum value of the sum of the distances between the transformed coordinate points and the coordinate points of the battery to be reconstructed as a target, reconstructing the incomplete area of the OCV curve, and estimating the capacity and the initial SOC value of each monomer in the battery pack. The battery capacity and initial SOC estimation method based on OCV curve reconstruction can provide data support for a BMS; in addition, the invention does not need to obtain any monomer capacity information in the battery pack, has the advantages of less initial conditions and wide application range.

Description

Lithium battery capacity and initial SOC estimation method based on OCV curve reconstruction
Technical Field
The invention relates to the technical field of lithium battery state estimation, in particular to a lithium battery capacity and initial SOC estimation method based on OCV curve reconstruction.
Background
The single batteries of the same batch and the same model have certain inevitable difference in the production and manufacturing process, so that the capacities, the internal resistances and other performances of the batteries have inconsistency, and the single batteries gradually age along with the continuous use of the batteries, so that the inconsistency of the performances of the batteries is increased. In the series battery pack, the phenomenon is more obvious, the inconsistency of the single battery capacity and the initial SOC restricts the capacity of the battery pack, and the potential safety hazard during the operation of the battery pack is increased.
The existing documents mostly only estimate the whole capacity of the battery pack through retrieval; or when the single battery capacity of the battery pack is estimated, the result of partial single battery capacity is required to be taken as a known condition, the single battery capacity cannot be estimated under the condition that the original structure of the battery pack is not disassembled, and the application range is narrow.
Disclosure of Invention
The invention aims to provide a lithium battery capacity and initial SOC estimation method based on OCV curve reconstruction, which solves the problem of potential safety hazard caused by the fact that the service life of a single battery cannot be known due to the fact that the capacity of the single battery is difficult to estimate under the condition that a battery pack is not disassembled, and improves the operation safety of the battery pack.
The technical scheme for realizing the purpose of the invention is as follows: a lithium battery single body capacity and initial SOC estimation method based on OCV curve reconstruction comprises the following steps:
step 1, carrying out a low-current capacity characteristic test experiment on a battery pack, and acquiring an IC curve of the current aging state of the battery by using a capacity increment method;
step 2, analyzing the battery IC curve to obtain characteristic peak and valley points of the battery IC curve; combining the characteristic peak and the valley point of the IC curve to obtain a characteristic point representing the OCV curve of the battery;
step 3, judging the incomplete area of the OCV curve of the battery;
step 4, performing OCV curve characteristic point transformation on single batteries in the battery pack; the minimum value of the sum of the distances between the transformed coordinate point and the coordinate point of the battery to be reconstructed is used as a target, and the reconstruction of the incomplete area of the OCV curve of the battery is realized;
and 5, estimating the battery capacity and the initial SOC value of the battery.
Compared with the prior art, the invention has the beneficial effects that: (1) the method for estimating the monomer capacity and the initial SOC of the battery pack based on the partial reconstruction of the OCV curve can estimate the monomer capacity in the battery pack on the basis of not disassembling the original structure of the battery pack and provide data support for a battery management system; (2) the method for estimating the monomer capacity and the initial SOC of the battery pack does not need to obtain any monomer capacity information in the battery pack, has the advantages of few initial conditions and wide application range.
Drawings
Fig. 1 is a flowchart of a battery cell capacity and initial SOC estimation method based on partial reconstruction of an OCV curve according to the present invention.
Fig. 2 is a graph of battery IC.
Fig. 3 is a schematic diagram of a battery OCV curve and corresponding OCV characteristic points.
Fig. 4 is a schematic diagram of the battery OCV curve region division.
Fig. 5 is an OCV graph of two batteries of different capacities.
Fig. 6 is a graph of battery OCV after conversion based on the optimum coefficient.
Fig. 7 is a schematic representation of a partially reconstructed OCV curve of a battery.
Detailed Description
As shown in fig. 1, a method for estimating the capacity and initial SOC of a lithium battery based on OCV curve reconstruction includes the following steps:
the method comprises the following steps: carrying out a low-current Capacity characteristic test experiment on the battery pack, and acquiring an IC curve of the current aging state of the battery by using an additive Capacity Analysis (ICA) method;
step two: analyzing the battery IC curve to obtain characteristic peak and valley points of the battery IC curve; combining the characteristic peak and the valley point of the IC curve to obtain a characteristic point representing the OCV curve of the battery;
step three: judging a defective area of the OCV curve of the battery by combining characteristic points of the OCV curve and initial and terminal voltages of the curve;
step four: carrying out OCV curve characteristic point transformation on battery monomers in the battery pack; the minimum value of the sum of the distances between the transformed coordinate point and the coordinate point of the battery to be reconstructed is used as a target, and the reconstruction of the incomplete area of the OCV curve of the battery is realized;
step five: the battery capacity and the initial SOC value of the battery are estimated.
Before estimating the battery capacity and the initial SOC value, firstly, a low-current capacity test experiment is carried out on the battery pack, and a capacity Increment (IC) curve of the current battery is obtained. Then, according to the peak and valley voltage values of the IC curve, the OCV characteristic point with the same voltage is searched in the OCV curve. And thirdly, dividing the OCV curve into a high-pressure area, a middle area and a low-pressure area according to the position of the characteristic point. And finally, according to the incomplete area of the OCV curve, performing OCV characteristic point transformation and calculating an optimal transformation coefficient.
And reconstructing the incomplete region of the OCV curve of the battery by using the optimal transformation coefficient obtained by calculation, calculating the capacity of the reconstructed region, and realizing the estimation of the total capacity and the initial SOC of the battery.
Further, the first step may obtain an IC curve of the battery in the current state according to the following steps:
step 1, charging the battery pack at a constant current of 1C/3 multiplying power, stopping charging when a single battery in the battery pack reaches an upper limit cut-off voltage, and naming the battery as n0(ii) a Wherein, C is the battery charging (discharging) multiplying power which is equal to the charging (discharging) current/the rated capacity of the battery in value.
And 2, standing for 2 hours.
And 3, discharging the battery at constant current with 1C/20 multiplying power, stopping discharging when a battery monomer in the battery pack reaches a lower limit cut-off voltage, and naming the battery as n1。
And 4, calculating the capacity variation dQ/dV along with the voltage according to the corresponding relation between the discharge capacity and the discharge voltage in the steps 1-3 to obtain a capacity increment and voltage relation curve.
And 5, obtaining a smooth data curve, namely a battery IC curve, by Gaussian filtering according to the capacity increment and voltage relation curve obtained in the step 1-4, wherein FIG. 1 is an IC curve graph after data processing.
Further, in the second step, the characteristic points of the OCV curve of the battery can be obtained according to the following steps:
step 1, collecting the X-axis coordinates of the characteristic points of the IC peak and the IC valley in the curve.
And 2, substituting the collected X-axis coordinates of the IC peak and valley characteristic points into the OCV curve to find out the corresponding OCV characteristic points.
The maximum number of the battery OCV characteristic points is 8.
Further, in the third step, the OCV curve incomplete region of the battery can be judged according to the following steps:
and dividing the OCV curve into three regions according to the magnitude of the voltage value of the abscissa of the OCV curve by using the characteristic points of the OCV curve of the battery obtained in the step two: a high pressure region, a middle region, and a low pressure region; the specific formula is as follows:
Figure BDA0002665423770000031
wherein, Vn0，begIs a battery n0Initial voltage value when discharging at 1C/20 multiplying power, namely maximum value of voltage on discharge OCV curve of all battery cells in battery pack, Vni，lIs a battery niAn abscissa value corresponding to a characteristic point 1 on the discharge OCV curve, i ∈ [0, N-1 ]]And i is an integer, Vni，8Is a battery niAbscissa value, V, corresponding to characteristic point 8 on discharge OCV curven1，endBattery n1The voltage value at the end of discharge at the rate of 1C/20, namely the minimum value of the voltages on the OCV curves of all the battery cells in the battery pack, is always V because the OCV characteristic points are the characteristic points of the battery on the OCV curves at different discharge stagesn1，end＜Vni，8＜Vni，l＜Vn0，beg。
Because the performances of the single batteries in the battery pack are inconsistent to a certain degree, the middle region of the OCV curve of the battery is complete, and the high-voltage region and the low-voltage region are usually incomplete, and the formula for judging the incomplete region of the OCV curve is as follows:
Figure BDA0002665423770000041
wherein, Vni，begFor cell n in the batteryiVoltage value, V, measured at the beginning of discharge at 1C/20 magnificationni，endFor cell n in the batteryiAnd measuring the voltage value at the end of the discharge at the rate of 1C/20.
Further, the characteristic point transformation in the step four is divided into characteristic point transformation in the incomplete state of the high-pressure area of the OCV curve and characteristic point transformation in the incomplete state of the low-pressure area of the OCV curve.
And fourthly, dividing the incomplete area of the reconstructed battery OCV curve into a high-voltage area of the reconstructed battery OCV curve and a low-voltage area of the reconstructed battery OCV curve.
Further, in the fourth step, the optimal coefficient of characteristic point transformation may be calculated according to the following steps, and an OCV partial curve is reconstructed, specifically:
step 1, determining characteristic point transformation formula of incomplete time of OCV curve high-pressure area
Figure BDA0002665423770000042
Wherein (V)n0，j，Ahn0，j) Is a battery n0(iv) coordinates of the characteristic point j of the OCV curve of (V)p，j，Ahp，j) The coordinate of the feature point j after coordinate transformation is represented by Δ u, which is a left-right translation coefficient for feature point transformation, i.e., a translation amount of a feature point voltage, Δ Ah, which is a top-bottom translation coefficient for feature point transformation, i.e., a translation amount of a feature point electric quantity, and k, which is a scaling coefficient.
Step 2, determining characteristic point transformation formula of incomplete time of low-voltage area of OCV curve
Figure BDA0002665423770000052
Wherein (V)n1，j，Ahn1，j) Is a battery n1The OCV curve characteristic point j.
Step 3, determining an objective function
Figure BDA0002665423770000051
Wherein m is the number of characteristic points of OCV curve participating in coordinate transformation, (V)ni，j，Ahni，j) Is a battery niThe coordinates of the characteristic point j of the OCV curve are solved by an objective function to obtain a group of optimal coefficients (delta u)fun，ΔAhfun，kfun)。
Step 4, when the high-voltage area of the OCV curve of the battery is reconstructed, the optimal coefficient is solved according to the step 1 and the step 3, and the battery n is used0After the high-voltage area data of the OCV curve is transformed by the coordinate in the step 1, the high-voltage area data of the OCV curve replaces the battery niAnd (3) incomplete high pressure area data of OCV curve.
Step 5, when the low-voltage area of the OCV curve of the battery is reconstructed, solving the optimal coefficient according to the step 2 and the step 3, and enabling the battery n1After the data of the low-voltage area of the OCV curve is transformed by the step 2 coordinate, the data replaces the battery niAnd (3) incomplete low-pressure region data of OCV curve.
Further, the fifth step may realize the estimation of the battery capacity and the initial SOC according to the following steps:
step 1, measuring battery n0The capacity of the high-voltage area of the OCV curve is combined with the obtained optimal coefficient to calculate the reconstructed battery niThe specific formula of the high-pressure area capacity of the OCV curve is as follows:
Cni，high＝(Ahn0，1-Ahn0，beg)×ki，fun0
wherein, Cni，highFor reconfiguring the cell niThe capacity of the high-pressure region of the OCV curve, Ahn0，1Is a battery n0The OCV curve characteristic point 1 of (1) corresponds to the ordinate value, Ahn0，begIs a battery n0Initial measured capacity at 1C/20 discharge, which is 0 Ah; k is a radical ofi，fun0To reconstruct electricityPool niThe resulting optimal scaling in the high voltage region.
Step 2, measuring the cell niThe specific formula of the capacity of the middle area of the OCV curve is as follows:
Cni，middle＝Ahni，end-Ahni，8
wherein, Cni，middleIs a battery niCapacity of the middle region of the OCV curve of (1), Ahni，endIs a battery niCapacity measured at the end of discharge at 1C/20 magnification, Ahni，8Is a battery niThe OCV characteristic point 8 of (2) is a vertical coordinate value.
Step 3, measuring the cell n1The capacity of the low-voltage area of the OCV curve is combined with the obtained optimal coefficient to calculate the reconstructed battery niThe specific formula of the low-pressure area capacity of the OCV curve is as follows:
Cni，low＝(Ahn1，end-Ahn1，8)×ki，fun1
wherein, Cni，lowFor reconfiguring the cell niCapacity of the low-pressure region of the OCV curve, Ahn1，endIs a battery n1The capacity measured at the end of discharge at 1C/20 magnification, which is the battery capacity; ahn1，8Is a battery n1The OCV curve characteristic point 8 of (2) corresponds to the ordinate value, ki，fun1For reconfiguring the cell niThe resulting optimal scaling factor in the low pressure region.
Step 4, estimating battery n of reconstructed OCV curveiThe specific formula of the total capacity is as follows:
Cni＝Cni，high+Cni，middle+Cni，low
wherein, CniFor the cell n to be estimatediThe total capacity of (c).
Step 5, estimating the battery niThe initial SOC has the following specific formula:
Figure BDA0002665423770000061
therein, SOCni，initialFor the cell n to be estimatediInitial SOC value of,. DELTA.Ahi，fun0For reconfiguring the cell niThe optimal up-down translation coefficient obtained in the high-pressure area.
The method determines the capacity of the single battery of the battery pack under the condition of not disassembling the battery pack, not only does not damage the original structure of the battery pack, but also has important significance for improving the safety of the battery pack; the method is suitable for estimating the single battery capacity of the battery pack under the vehicle-mounted condition.
The present invention will be specifically described below by taking a lithium iron phosphate battery as an example.
Examples
N lithium iron phosphate batteries of certain types are selected to be connected in series to form a battery pack for experiment. Firstly, according to a manual provided by a manufacturer, charging the battery to a certain monomer upper limit cut-off voltage of 3.65V at a rate of 1C/3, wherein C is 20A, and then discharging to a certain monomer lower limit cut-off voltage of 2V at a constant current at a rate of 1C/20 to finish a battery low current IC test, wherein the specific process is as follows:
step 1, charging the battery pack at a constant current of 1C/3 multiplying power, stopping charging when a single battery in the battery pack reaches an upper limit cut-off voltage, and naming the battery as n0。
And 2, standing for 2 hours.
And 3, discharging the battery at constant current with 1C/20 multiplying power, stopping discharging when a battery monomer in the battery pack reaches a lower limit cut-off voltage, and naming the battery as n1。
And 4, calculating the capacity variation dQ/dV along with the voltage according to the corresponding relation between the discharge capacity and the discharge voltage in the step 1-3, taking the discharge voltage as an x axis, and taking the capacity variation dQ/dV along with the voltage as a y axis to obtain a capacity increment and voltage relation curve.
And 5, obtaining a capacity increment and voltage relation curve according to the steps 1-4, and obtaining a smooth data curve, namely a battery IC curve by using Gaussian filtering, as shown in figure 2.
And establishing an OCV curve of the battery by taking the voltage of the measured battery as an x axis and the capacity as a y axis.
Collecting the x-axis coordinates of the characteristic points of the IC peak and the IC valley in the IC curve; then, the characteristic points with the same x-axis coordinates as the IC peaks and valleys are found in the OCV curve as the characteristic points of the OCV curve of the battery, as shown in fig. 3.
According to the characteristic points of the obtained battery OCV curve, the OCV curve is divided into three regions according to the magnitude of the voltage value on the abscissa of the OCV curve: the high pressure zone, the middle zone and the low pressure zone are shown in fig. 4; the specific formula is as follows:
Figure BDA0002665423770000071
wherein, Vn0，begIs a battery n0Initial voltage value when discharging at 1C/20 multiplying power, namely maximum value of voltage on discharge OCV curve of all battery cells in battery pack, Vni，lIs a battery niAn abscissa value corresponding to a characteristic point 1 on the discharge OCV curve, i ∈ [0, N-1 ]]And i is an integer, Vni，8Is a battery niAbscissa value, V, corresponding to characteristic point 8 on discharge OCV curven1，endIs a battery n1And the voltage value at the end of the discharge at the rate of 1C/20, namely the minimum value of the voltages on the discharge OCV curves of all the battery cells in the battery pack.
Because the performances of the battery cells in the battery pack are inconsistent to a certain degree, the middle region of the OCV curve of the battery is complete, the high-voltage region and the low-voltage region are usually incomplete, and the incomplete region of the OCV curve of the battery is reconstructed conveniently in the following process, so that the incomplete region of the OCV curve needs to be judged, and the specific formula is as follows:
Figure BDA0002665423770000081
wherein, Vni，begFor cell n in the batteryiVoltage value, V, measured at the beginning of discharge at 1C/20 magnificationni，endFor cell n in the batteryiAnd measuring the voltage value at the end of the discharge at the rate of 1C/20.
If cell niThe OCV curve of (2) is incomplete in the high-pressure region; due to the battery n0The high-pressure region of the OCV curve is complete, and therefore n is used0The high-voltage region of the OCV curve is used as a reconstructed cell niThe basis of the high-pressure region of the OCV curve of (1); the characteristic points of the OCV curve in the high-pressure area are transformed, and the specific formula is as follows:
Figure BDA0002665423770000082
wherein (V)n0，j，Ahn0，j) Is a battery n0(iv) coordinates of the characteristic point j of the OCV curve of (V)p，j，Ahp，j) The coordinate of the feature point j after coordinate transformation is represented by Δ u, which is a left-right translation coefficient for feature point transformation, i.e., a translation amount of a feature point voltage, Δ Ah, which is a top-bottom translation coefficient for feature point transformation, i.e., a translation amount of a feature point electric quantity, and k, which is a scaling coefficient.
If cell niThe OCV curve low-voltage region of (a) is incomplete; due to the battery n1The low-pressure region of the OCV curve is complete, and therefore n is used1The low-voltage region of the OCV curve is used as a reconstructed cell niThe basis of the low-pressure region of the OCV curve of (1); the characteristic points of the OCV curve in the low-voltage area are transformed, and the specific formula is as follows:
Figure BDA0002665423770000083
wherein (V)n1，j，Ahn1，j) Is a battery n1The OCV curve characteristic point j.
Taking the minimum value of the sum of the distances between the transformed coordinate point and the coordinate point of the battery to be reconstructed as a target, constructing a target function G, and solving the minimum value of the target function to obtain the optimal transformation coefficient of the reconstruction curve, wherein the specific target function is as follows:
Figure BDA0002665423770000084
wherein m is the number of characteristic points of the OCV curve participating in coordinate transformation, considering the aging of the battery,the collection of the peak and valley characteristics of the IC is influenced, and the value of m is preferably 5; (V)i，j，Ahi，j) Is a battery niThe OCV curve characteristic point j is solved by an objective function to obtain a group of optimal coefficients (delta ufun，ΔAhfun，kfun)。
Fig. 5 is a graph showing OCV curves of two batteries having different capacities, in which the high-voltage region of battery 1 is complete and the optimal conversion coefficient is obtained after the OCV characteristic points are converted, and fig. 6 is a result of converting the OCV curve of battery 1, and it can be seen that the overlapping rate of the converted OCV curve of battery 1 and the OCV curve of battery 2 is high.
If the battery n is reconstructediOCV curve high pressure region of (1); using a battery niThe optimal transformation coefficient obtained by the coordinate transformation of the incomplete time of the high-voltage area of the OCV curve is used for transforming the battery n0The data of the high-voltage area of the OCV curve are transformed by coordinates to replace the battery niThe OCV curve of (1) high pressure region data.
If the battery n is reconstructediOCV curve low-pressure region of (1); using a battery niThe optimal transformation coefficient obtained by the coordinate transformation of the incomplete time of the low-voltage area of the OCV curve is used for transforming the battery n1The data of the low-voltage area of the OCV curve are transformed by coordinates to replace the battery niThe OCV curve of (1) low-pressure region data.
The final cell partial OCV curve reconstruction result is shown in fig. 7, where both the high-voltage region and the low-voltage region of the battery 2 are missing in fig. 7. Through the reconstruction of the high-voltage area and the low-voltage area of the OCV curve, the OCV curve of the battery is complete, and the supplemented OCV curve part is tightly jointed with the original OCV curve of the battery.
Measuring cell n0The capacity of the high-voltage area of the OCV curve is combined with the obtained optimal coefficient to calculate the reconstructed battery niThe specific formula of the high-pressure area capacity of the OCV curve is as follows:
Cni，high＝(Ahn0，1-Ahn0，beg)×ki，fun0
wherein, Cni，highFor reconfiguring the cell niThe capacity of the high-pressure region of the OCV curve, Ahn0，1Is a battery n0Characteristic point 1 of the OCV curve corresponds toOrdinate value of (a)n0，begIs a battery n0Initial measured capacity at 1C/20 discharge; k is a radical ofi，fun0The optimal scaling factor obtained when reconstructing the high voltage region of battery i.
Measuring cell niThe specific formula of the capacity of the middle area of the OCV curve is as follows:
Cni，middle＝Ahni，end-Ahni，8
wherein, Cni，middleIs a battery niCapacity of the middle region of the OCV curve of (1), Ahni，endIs a battery niCapacity measured at the end of discharge at 1C/20 magnification, Ahni，8Is a battery niThe OCV characteristic point 8 of (2) is a vertical coordinate value.
Measuring cell n1The capacity of the low-voltage area of the OCV curve is combined with the obtained optimal coefficient to calculate the reconstructed battery niThe specific formula of the low-pressure area capacity of the OCV curve is as follows:
Cni，low＝(Ahn1，end-Ahn1，8)×ki，fun1
wherein, Cni，lowFor reconfiguring the cell niCapacity of the low-pressure region of the OCV curve, Ahn1，endIs a battery n1The capacity measured at the end of discharge at 1C/20 magnification, which is the battery capacity; ahn1，8Is a battery n1The OCV curve characteristic point 8 of (2) corresponds to the ordinate value, ki，fun1For reconfiguring the cell niThe resulting optimal scaling factor in the low pressure region.
Estimating cell n of reconstructed OCV curveiThe specific formula of the total capacity is as follows:
Cni＝Cni，high+Cni，middle+Cni，low
wherein, CniFor the cell n to be estimatediThe total capacity of (c).
Estimating a battery niThe specific formula of the initial SOC value is as follows:
Figure BDA0002665423770000101
therein, SOCni，initialFor the cell n to be estimatediInitial SOC value of,. DELTA.Ahi，fun0For reconfiguring the cell niThe optimal up-down translation coefficient obtained in the high-pressure area.
In one example of the present invention, the discharge OCV curve of three-section nominal 20Ah cells after grouping was verified by the method of the present invention, and the experimental results are shown in table 1. It can be seen that the capacity estimation accuracy is within 3% and the initial SOC error is within 4%.
TABLE 1 Battery cell Capacity and initial SOC estimation test results
Figure BDA0002665423770000102
Figure BDA0002665423770000111

Claims (10)

1. A lithium battery capacity and initial SOC estimation method based on OCV curve reconstruction includes N to-be-estimated battery monomers of the same type and the same batch in a battery pack under the condition that the battery pack is not disassembled, and the monomers are connected in series to form a group, and the method is characterized by comprising the following steps of:
step 1, carrying out a low-current capacity characteristic test experiment on a battery pack, and acquiring an IC curve of the current aging state of the battery by using a capacity increment method;
step 2, analyzing the battery IC curve to obtain characteristic peak and valley points of the battery IC curve; combining the characteristic peak and the valley point of the IC curve to obtain a characteristic point representing the OCV curve of the battery;
step 3, judging the incomplete area of the OCV curve of the battery;
step 4, performing OCV curve characteristic point transformation on single batteries in the battery pack; the minimum value of the sum of the distances between the transformed coordinate point and the coordinate point of the battery to be reconstructed is used as a target, and the reconstruction of the incomplete area of the OCV curve of the battery is realized;
and 5, estimating the battery capacity and the initial SOC value of the battery.
2. The lithium battery capacity and initial SOC estimation method based on OCV curve reconstruction according to claim 1, wherein step 1 is to perform a low current capacity characteristic test experiment on the battery pack, and obtain an IC curve of a current aging state of the battery by using a capacity increment method, specifically:
step 1-1, the battery pack is charged at constant current with 1C/3 multiplying power, when a battery monomer in the battery pack reaches an upper limit cut-off voltage, the battery pack stops charging, and the battery is named as n0(ii) a Wherein C is the charge-discharge multiplying power of the battery, and is equal to the charge-discharge current/rated capacity of the battery in numerical value;
step 1-2, standing for 2 hours;
step 1-3, discharging the battery pack at a constant current with a rate of 1C/20, stopping discharging when a single battery in the battery pack reaches a lower limit cut-off voltage, and naming the battery as n1；
Step 1-4, calculating the capacity variation dQ/dV along with the voltage according to the corresponding relation between the discharge capacity and the discharge voltage in the step 1-3 to obtain a capacity increment and voltage relation curve;
and 1-5, obtaining a smooth data curve, namely a battery IC curve, by Gaussian filtering according to the capacity increment and voltage relation curve obtained in the step 1-4.
3. The lithium battery capacity and initial SOC estimation method based on OCV curve reconstruction according to claim 2, wherein step 2 analyzes the battery IC curve to obtain characteristic peaks and valley points of the battery IC curve, and obtains characteristic points representing the OCV curve of the battery by combining the characteristic peaks and valley points of the IC curve, specifically:
step 2-1, collecting the x-axis coordinates of the characteristic points of the IC peak and the IC valley in the curve;
and 2-2, substituting the collected X-axis coordinates of the characteristic points of the peak and the valley of the IC into the OCV curve to find the characteristic points of the OCV curve corresponding to the characteristic points.
4. The lithium battery capacity and initial SOC estimation method based on OCV curve reconstruction as claimed in claim 3, characterized in that the number of characteristic points of the OCV curve of the battery is 8 at maximum.
5. The lithium battery capacity and initial SOC estimation method based on OCV curve reconstruction according to claim 2, wherein step 3 is specifically:
and (3) dividing the OCV curve into three regions according to the magnitude of the voltage value of the abscissa of the OCV curve by using the characteristic points of the OCV curve of the battery obtained in the step (2): a high pressure region, a middle region, and a low pressure region; the specific formula is as follows:
Figure FDA0002665423760000021
wherein, Vn0,begIs a battery n0Initial voltage value when discharging at 1C/20 multiplying power, namely maximum value of voltage on discharge OCV curve of all battery cells in battery pack, Vni,1Is a battery niAn abscissa value corresponding to a characteristic point 1 on the discharge OCV curve, i ∈ [0, N-1 ]]And i is an integer, Vni,8Is a battery niAbscissa value, V, corresponding to characteristic point 8 on discharge OCV curven1,endIs a battery n1And the voltage value at the end of the discharge at the rate of 1C/20, namely the minimum value of the voltages on the discharge OCV curves of all the battery cells in the battery pack.
6. The lithium battery capacity and initial SOC estimation method based on OCV curve reconstruction according to claim 5, characterized in that the area of OCV curve deformity is determined by the following specific formula:
Figure FDA0002665423760000022
wherein, Vni,begFor cell n in the batteryiVoltage value, V, measured at the beginning of discharge at 1C/20 magnificationni,endFor cells in battery packsniAnd measuring the voltage value at the end of the discharge at the rate of 1C/20.
7. The method for estimating the capacity and the initial SOC of the lithium battery based on the OCV curve reconstruction as claimed in claim 6, wherein the characteristic point transformation in step 4 is divided into an OCV curve high-voltage region incomplete time characteristic point transformation and an OCV curve low-voltage region incomplete time characteristic point transformation.
8. The lithium battery capacity and initial SOC estimation method based on OCV curve reconstruction according to claim 7, wherein the OCV curve incomplete region of the reconstructed battery in step 4 is divided into an OCV curve high-voltage region of the reconstructed battery and an OCV curve low-voltage region of the reconstructed battery.
9. The lithium battery capacity and initial SOC estimation method based on OCV curve reconstruction according to claim 8, wherein step 4 is specifically:
step 4-1, characteristic points of the OCV curve in the high-pressure area are transformed in the incomplete state, and the specific formula is as follows:
Figure FDA0002665423760000031
wherein (V)n0,j，Ahn0,j) Is a battery n0(iv) coordinates of the characteristic point j of the OCV curve of (V)p,j，Ahp,j) The coordinate of the feature point j after coordinate transformation is represented by Δ u, which is a left-right translation coefficient for feature point transformation, i.e., a translation amount of a feature point voltage, Δ Ah, which is a top-bottom translation coefficient for feature point transformation, i.e., a translation amount of a feature point electric quantity, and k, which is a scaling coefficient;
step 4-2, characteristic points of the OCV curve in the low-pressure area are transformed in the incomplete state, and the specific formula is as follows:
Figure FDA0002665423760000032
wherein (V)n1,j，Ahn1,j) Is a battery n1The coordinates of the OCV curve characteristic point j of (1);
4-3, in order to obtain the optimal transformation coefficient, solving the minimum value of an objective function by constructing the objective function, wherein the specific objective function is as follows:
Figure FDA0002665423760000033
wherein m is the number of characteristic points of OCV curve participating in coordinate transformation, (V)ni,j，Ahni,j) Is a battery niThe coordinates of the characteristic point j of the OCV curve are solved by an objective function to obtain a group of optimal coefficients (delta u)fun,ΔAhfun,kfun)；
Step 4-4, when the high-voltage area of the OCV curve of the battery is reconstructed, solving the optimal coefficient by using a formula (3) and a formula (5), and enabling the battery n0After the OCV curve high-voltage area data is transformed by the coordinate of the formula (3), the battery n is replacediIncomplete high-pressure area data of the OCV curve of (1);
step 4-5, when the low-voltage area of the OCV curve of the battery is reconstructed, the optimal coefficient is solved by using the formula (4) and the formula (5), and the battery n is processed1After the data of the low-voltage area of the OCV curve is transformed by the coordinate of the formula (4), the data replaces the battery niAnd (3) incomplete low-pressure region data of OCV curve.
10. The OCV curve reconstruction-based lithium battery capacity and initial SOC estimation method according to claim 9, wherein the step 5 of estimating the battery capacity and the initial SOC value comprises the following specific steps:
step 5-1, measuring cell n0The capacity of the high-voltage area of the OCV curve is combined with the obtained optimal coefficient to calculate the reconstructed battery niThe specific formula of the high-pressure area capacity of the OCV curve is as follows:
Cni,high＝(Ahn0,1-Ahn0,beg)×ki,fun0 (6)
wherein, Cni,highFor reconfiguring the cell niCapacity of high pressure region of OCV curveAmount, Ahn0,1Is a battery n0The OCV curve characteristic point 1 of (1) corresponds to the ordinate value, Ahn0,begIs a battery n0Initial measured capacity at 1C/20 discharge; k is a radical ofi,fun0For reconfiguring the cell niThe optimal scaling factor obtained in the high-voltage area;
step 5-2, measuring cell niThe specific formula of the capacity of the middle area of the OCV curve is as follows:
Cni,middle＝Ahni,end-Ahni,8 (7)
wherein, Cni,middleIs a battery niCapacity of the middle region of the OCV curve of (1), Ahni,endIs a battery niCapacity measured at the end of discharge at 1C/20 magnification, Ahni,8Is a battery niThe OCV characteristic point 8 of (2);
step 5-3, measuring cell n1The capacity of the low-voltage area of the OCV curve is combined with the obtained optimal coefficient to calculate the reconstructed battery niThe specific formula of the low-pressure area capacity of the OCV curve is as follows:
Cni,low＝(Ahn1,end-Ahn1,8)×ki,fun1 (8)
wherein, Cni,lowFor reconfiguring the cell niCapacity of the low-pressure region of the OCV curve, Ahn1,endIs a battery n1The capacity measured at the end of discharge at 1C/20 magnification, which is the battery capacity; ahn1,8Is a battery n1The OCV curve characteristic point 8 of (2) corresponds to the ordinate value, ki,fun1For reconfiguring the cell niThe optimal scaling factor obtained in the low-voltage area;
step 5-4, estimating the battery n of the reconstructed OCV curveiThe specific formula of the total capacity is as follows:
Cni＝Cni,high+Cni,middle+Cni,low (9)
wherein, CniFor the cell n to be estimatediThe total capacity of (c);
step 5-5, estimating the cell niThe specific formula of the initial SOC value is as follows:
Figure FDA0002665423760000051
therein, SOCni,initialFor the cell n to be estimatediInitial SOC value of,. DELTA.Ahi,fun0For reconfiguring the cell niThe optimal up-down translation coefficient obtained in the high-pressure area.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112698217A (en) * 2020-12-25 2021-04-23 江苏省特种设备安全监督检验研究院 Battery monomer capacity estimation method based on particle swarm optimization algorithm
CN113311338A (en) * 2021-06-02 2021-08-27 江苏大学 Data-driven lithium ion battery open circuit voltage curve reconstruction method
CN113759251A (en) * 2021-08-17 2021-12-07 江苏大学 Cloud battery pack capacity consistency analysis method based on similar capacity increment curve
CN114264964A (en) * 2021-12-30 2022-04-01 蜂巢能源科技(无锡)有限公司 Method, device, equipment and medium for evaluating battery capacity

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2990818A2 (en) * 2014-09-01 2016-03-02 Yokogawa Electric Corporation Secondary battery capacity measurement system and secondary battery capacity measurement method
KR20160026766A (en) * 2014-09-01 2016-03-09 요코가와 덴키 가부시키가이샤 Secondary battery capacity measurement system and secondary battery capacity measurement method
EP3196663A1 (en) * 2016-01-21 2017-07-26 Yokogawa Electric Corporation Secondary battery capacity measuring system and secondary battery capacity measuring method
CN107450031A (en) * 2017-08-07 2017-12-08 北京理工大学 A kind of reconstructing method of electrokinetic cell system OCV SOC functional relations
CN109901083A (en) * 2019-04-01 2019-06-18 北京理工大学 A kind of power battery OCV curve on-line reorganization method
CN110058175A (en) * 2019-05-05 2019-07-26 北京理工大学 A kind of reconstructing method of power battery open-circuit voltage-state-of-charge functional relation
CN110927591A (en) * 2019-12-11 2020-03-27 北京理工大学 Battery capacity estimation method, computer readable medium and vehicle
CN111077465A (en) * 2019-12-25 2020-04-28 欣旺达电动汽车电池有限公司 Battery characteristic parameter extraction method and device, computer equipment and storage medium
CN111142036A (en) * 2019-12-18 2020-05-12 同济大学 Lithium ion battery online rapid capacity estimation method based on capacity increment analysis
CN111398833A (en) * 2020-03-13 2020-07-10 浙江大学 Battery health state evaluation method and evaluation system

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2990818A2 (en) * 2014-09-01 2016-03-02 Yokogawa Electric Corporation Secondary battery capacity measurement system and secondary battery capacity measurement method
KR20160026766A (en) * 2014-09-01 2016-03-09 요코가와 덴키 가부시키가이샤 Secondary battery capacity measurement system and secondary battery capacity measurement method
EP3196663A1 (en) * 2016-01-21 2017-07-26 Yokogawa Electric Corporation Secondary battery capacity measuring system and secondary battery capacity measuring method
CN107450031A (en) * 2017-08-07 2017-12-08 北京理工大学 A kind of reconstructing method of electrokinetic cell system OCV SOC functional relations
CN109901083A (en) * 2019-04-01 2019-06-18 北京理工大学 A kind of power battery OCV curve on-line reorganization method
CN110058175A (en) * 2019-05-05 2019-07-26 北京理工大学 A kind of reconstructing method of power battery open-circuit voltage-state-of-charge functional relation
CN110927591A (en) * 2019-12-11 2020-03-27 北京理工大学 Battery capacity estimation method, computer readable medium and vehicle
CN111142036A (en) * 2019-12-18 2020-05-12 同济大学 Lithium ion battery online rapid capacity estimation method based on capacity increment analysis
CN111077465A (en) * 2019-12-25 2020-04-28 欣旺达电动汽车电池有限公司 Battery characteristic parameter extraction method and device, computer equipment and storage medium
CN111398833A (en) * 2020-03-13 2020-07-10 浙江大学 Battery health state evaluation method and evaluation system

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112698217A (en) * 2020-12-25 2021-04-23 江苏省特种设备安全监督检验研究院 Battery monomer capacity estimation method based on particle swarm optimization algorithm
CN112698217B (en) * 2020-12-25 2023-11-03 江苏省特种设备安全监督检验研究院 Battery monomer capacity estimation method based on particle swarm optimization algorithm
CN113311338A (en) * 2021-06-02 2021-08-27 江苏大学 Data-driven lithium ion battery open circuit voltage curve reconstruction method
CN113311338B (en) * 2021-06-02 2023-01-17 江苏大学 Data-driven lithium ion battery open circuit voltage curve reconstruction method
CN113759251A (en) * 2021-08-17 2021-12-07 江苏大学 Cloud battery pack capacity consistency analysis method based on similar capacity increment curve
CN114264964A (en) * 2021-12-30 2022-04-01 蜂巢能源科技(无锡)有限公司 Method, device, equipment and medium for evaluating battery capacity
CN114264964B (en) * 2021-12-30 2023-10-20 蜂巢能源科技(无锡)有限公司 Method, device, equipment and medium for evaluating battery capacity

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